Experiment 2 - analysis delta RTs
Didino Daniele
October 2021
This file is part of the supplementary material of the manuscript: Didino, D., Brandtner, M., & Knops, A. (2021). No influence of masked priming on the multiplication fact retrieval in a result verification task.
This script reports the analysis for experiment 2.
Dataset loaded: exp2_data.rds (data of result verification task)
Load libraries, my functions (my_functions folder) and data (data/processed folder):
library('ggpubr')
#> Loading required package: ggplot2
library('here')
#> here() starts at D:/mult_prime
library('kableExtra')
# library('knitr')
library('plotly')
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#> Attaching package: 'plotly'
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#> last_plot
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#> filter
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#> layout
library('ggridges')
library('tidyverse')
#> -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
#> v tibble 3.1.0 v dplyr 1.0.5
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#> v purrr 0.3.4
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library('BayesFactor')
#> Loading required package: coda
#> Loading required package: Matrix
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#> Attaching package: 'Matrix'
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#> expand, pack, unpack
#> ************
#> Welcome to BayesFactor 0.9.12-4.2. If you have questions, please contact Richard Morey (richarddmorey@gmail.com).
#>
#> Type BFManual() to open the manual.
#> ************
# Load my functions
source(here('funcs', 'load_my_functions.R'))
# Load data
exp2 <- readRDS(here('data', 'processed', 'exp2_data.rds'))Exclude outliers
exp2 <-
exp2 %>%
filter(outlier == FALSE)Calculate delta RTs
exp2_dRT <-
exp2 %>%
return_delta_RT(.,
y_col = RT,
cols_to_group = c('sj', 'problem_size', 'SOA'),
cols_to_subtract = c('prime_cond'),
neutral_cond = 'neutral')
#> `summarise()` has grouped output by 'sj', 'problem_size', 'SOA'. You can override using the `.groups` argument.Delta RTs versus RTs
dens_RT <-
ggplot(exp2, aes(RT)) +
geom_density() +
stat_function(fun = dnorm,
args = list(mean = mean(exp2$RT), sd = sd(exp2$RT)),
color = 'blue') +
ggtitle('')
dens_dRT <-
ggplot(exp2_dRT, aes(dRT)) +
geom_density() +
stat_function(fun = dnorm,
args = list(mean = mean(exp2_dRT$dRT), sd = sd(exp2_dRT$dRT)),
color = 'blue') +
ggtitle('')
ggarrange(dens_RT,
dens_dRT,
ncol = 2,
nrow = 1,
labels = c('RT', 'delta RT'))
Delta RTs table
Table with RTs mean, standard deviation and standard error (aggregated on subject and condition):
# Calculate statistics
exp2_stats <-
exp2_dRT %>%
return_stats(c('problem_size', 'SOA', 'cond'),
DV = 'dRT')
#> `summarise()` has grouped output by 'sj', 'problem_size', 'SOA'. You can override using the `.groups` argument.
#> `summarise()` has grouped output by 'problem_size', 'SOA'. You can override using the `.groups` argument.
# Show results
exp2_stats %>%
make_table('Statistics (prime condition by problem size)')| problem_size | SOA | cond | N_sj | Mean | SD | SE |
|---|---|---|---|---|---|---|
| large | SOA_070 | neigh_con | 28 | 10.55 | 61.85 | 11.69 |
| large | SOA_070 | neigh_inc | 28 | 23.32 | 64.60 | 12.21 |
| large | SOA_070 | unrel_con | 28 | 5.29 | 49.72 | 9.40 |
| large | SOA_070 | unrel_inc | 28 | 16.80 | 81.24 | 15.35 |
| large | SOA_120 | neigh_con | 28 | 10.35 | 60.30 | 11.39 |
| large | SOA_120 | neigh_inc | 28 | 47.80 | 57.17 | 10.80 |
| large | SOA_120 | unrel_con | 28 | 13.15 | 61.29 | 11.58 |
| large | SOA_120 | unrel_inc | 28 | 35.58 | 60.79 | 11.49 |
| large | SOA_170 | neigh_con | 28 | 12.58 | 77.64 | 14.67 |
| large | SOA_170 | neigh_inc | 28 | 24.93 | 60.76 | 11.48 |
| large | SOA_170 | unrel_con | 28 | 5.39 | 71.40 | 13.49 |
| large | SOA_170 | unrel_inc | 28 | 28.90 | 67.85 | 12.82 |
| small | SOA_070 | neigh_con | 28 | -1.18 | 39.70 | 7.50 |
| small | SOA_070 | neigh_inc | 28 | 16.52 | 50.88 | 9.61 |
| small | SOA_070 | unrel_con | 28 | 8.25 | 52.78 | 9.97 |
| small | SOA_070 | unrel_inc | 28 | 23.66 | 64.39 | 12.17 |
| small | SOA_120 | neigh_con | 28 | -2.03 | 50.90 | 9.62 |
| small | SOA_120 | neigh_inc | 28 | 8.23 | 38.63 | 7.30 |
| small | SOA_120 | unrel_con | 28 | -2.16 | 59.96 | 11.33 |
| small | SOA_120 | unrel_inc | 28 | 9.76 | 62.62 | 11.84 |
| small | SOA_170 | neigh_con | 28 | 31.51 | 52.51 | 9.92 |
| small | SOA_170 | neigh_inc | 28 | 24.55 | 52.07 | 9.84 |
| small | SOA_170 | unrel_con | 28 | 28.17 | 54.72 | 10.34 |
| small | SOA_170 | unrel_inc | 28 | 25.41 | 46.90 | 8.86 |
Delta RTs plots
RTs distribution across prime conditions:
plot_1 <-
exp2_dRT %>%
ggplot(aes(dRT, fill = cond)) +
geom_density(alpha = 0.6) +
facet_wrap(~ SOA + problem_size, ncol = 2) +
theme(panel.spacing = unit(2, "cm"))
ggplotly(plot_1)exp2_dRT %>%
ggplot(aes(x = dRT, y = cond, fill = stat(x))) +
geom_density_ridges_gradient(scale = 3) +
scale_fill_viridis_c(name = 'RTs', option = 'C') +
labs(y = 'Prime condition') +
facet_wrap(~ SOA + problem_size, ncol = 2)
#> Picking joint bandwidth of 26.6
#> Picking joint bandwidth of 18.9
#> Picking joint bandwidth of 20.7
#> Picking joint bandwidth of 15.8
#> Picking joint bandwidth of 27.2
#> Picking joint bandwidth of 22.5
Bayes factor
Bayes factors are computer with the package BayesFactor.
tribble(
~BF, ~Evidence,
'0-3', 'Anectodal',
'3-10', 'Moderate',
'10-30', 'Strong',
'30-100', 'Very_strong',
'>100', 'Decisive') %>%
make_table('Bayes factor interpretation')| BF | Evidence |
|---|---|
| 0-3 | Anectodal |
| 3-10 | Moderate |
| 10-30 | Strong |
| 30-100 | Very_strong |
| >100 | Decisive |
exp2_bf <-
exp2_dRT %>%
group_by(problem_size, SOA, cond) %>%
nest() %>%
mutate(
# "DV" is the column with the means returned by "return_means()"
BF_test = map(data,
~ttestBF(x = .$dRT,
mu = 0)),
BF_value = map(BF_test,
~extractBF(.) %>%
tibble %>%
select(bf, error))
) %>%
unnest(BF_value)
exp2_bf
#> # A tibble: 24 x 7
#> # Groups: problem_size, SOA, cond [24]
#> problem_size SOA cond data BF_test bf error
#> <chr> <chr> <chr> <list> <list> <dbl> <dbl>
#> 1 large SOA_070 neigh_con <tibble[,2] [28 ~ <BFBysFc~ 0.291 1.58e-4
#> 2 large SOA_070 neigh_inc <tibble[,2] [28 ~ <BFBysFc~ 0.978 1.25e-7
#> 3 large SOA_070 unrel_con <tibble[,2] [28 ~ <BFBysFc~ 0.232 8.67e-5
#> 4 large SOA_070 unrel_inc <tibble[,2] [28 ~ <BFBysFc~ 0.345 2.03e-4
#> 5 large SOA_120 neigh_con <tibble[,2] [28 ~ <BFBysFc~ 0.292 1.60e-4
#> 6 large SOA_120 neigh_inc <tibble[,2] [28 ~ <BFBysFc~ 191. 1.07e-7
#> 7 large SOA_120 unrel_con <tibble[,2] [28 ~ <BFBysFc~ 0.359 2.12e-4
#> 8 large SOA_120 unrel_inc <tibble[,2] [28 ~ <BFBysFc~ 9.05 1.35e-5
#> 9 large SOA_170 neigh_con <tibble[,2] [28 ~ <BFBysFc~ 0.280 1.48e-4
#> 10 large SOA_170 neigh_inc <tibble[,2] [28 ~ <BFBysFc~ 1.50 1.10e-7
#> # ... with 14 more rows
exp2_bf <-
exp2_bf %>%
select(problem_size, SOA, cond, bf) %>%
rename(BF_10 = bf) %>%
mutate(
BF_01 = 1 / BF_10
)
# Show results
exp2_bf %>%
make_table('Bayes factor')| problem_size | SOA | cond | BF_10 | BF_01 |
|---|---|---|---|---|
| large | SOA_070 | neigh_con | 0.29 | 3.44 |
| large | SOA_070 | neigh_inc | 0.98 | 1.02 |
| large | SOA_070 | unrel_con | 0.23 | 4.31 |
| large | SOA_070 | unrel_inc | 0.34 | 2.90 |
| large | SOA_120 | neigh_con | 0.29 | 3.43 |
| large | SOA_120 | neigh_inc | 191.02 | 0.01 |
| large | SOA_120 | unrel_con | 0.36 | 2.79 |
| large | SOA_120 | unrel_inc | 9.05 | 0.11 |
| large | SOA_170 | neigh_con | 0.28 | 3.57 |
| large | SOA_170 | neigh_inc | 1.50 | 0.67 |
| large | SOA_170 | unrel_con | 0.22 | 4.63 |
| large | SOA_170 | unrel_inc | 1.73 | 0.58 |
| small | SOA_070 | neigh_con | 0.20 | 4.93 |
| small | SOA_070 | neigh_inc | 0.73 | 1.36 |
| small | SOA_070 | unrel_con | 0.27 | 3.65 |
| small | SOA_070 | unrel_inc | 1.03 | 0.97 |
| small | SOA_120 | neigh_con | 0.20 | 4.89 |
| small | SOA_120 | neigh_inc | 0.36 | 2.81 |
| small | SOA_120 | unrel_con | 0.20 | 4.91 |
| small | SOA_120 | unrel_inc | 0.27 | 3.66 |
| small | SOA_170 | neigh_con | 10.72 | 0.09 |
| small | SOA_170 | neigh_inc | 2.69 | 0.37 |
| small | SOA_170 | unrel_con | 4.20 | 0.24 |
| small | SOA_170 | unrel_inc | 5.60 | 0.18 |
Plot dRT against mean RT (by subject)
Create a single data frame with dRT and mean RT (a data point for each subject, problem size and prime condition)
# Mean RT by subject
exp2_meanRT_sj <-
exp2 %>%
return_stats(c('sj', 'problem_size', 'SOA', 'cond'),
DV = 'RT') %>%
select(sj, problem_size, SOA, prime_cond, Mean) %>%
rename(cond = prime_cond) %>%
filter(cond != 'neutral')
#> `summarise()` has grouped output by 'sj', 'problem_size', 'SOA'. You can override using the `.groups` argument.
#> `summarise()` has grouped output by 'sj', 'problem_size', 'SOA'. You can override using the `.groups` argument.
# Join data frames
exp2_dRT_meanRT <-
full_join(exp2_dRT,
exp2_meanRT_sj,
by = c('sj', 'problem_size', 'SOA', 'cond'))Plot for large problems
exp2_dRT_meanRT %>%
filter(problem_size == 'large') %>%
ggplot(aes(Mean, dRT)) +
geom_point() +
geom_hline(yintercept = 0,
linetype = 'dashed',
color = 'red',
size = 0.5) +
facet_wrap(~ cond + SOA, ncol = 3)
Plot for small problems
exp2_dRT_meanRT %>%
filter(problem_size == 'small') %>%
ggplot(aes(Mean, dRT)) +
geom_point() +
geom_hline(yintercept = 0,
linetype = 'dashed',
color = 'red',
size = 0.5) +
facet_wrap(~ cond + SOA, ncol = 3)
session information
xfun::session_info()
#> R version 4.0.5 (2021-03-31)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 19044)
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#> Locale:
#> LC_COLLATE=English_United Kingdom.1252
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#> LC_MONETARY=English_United Kingdom.1252
#> LC_NUMERIC=C
#> LC_TIME=English_United Kingdom.1252
#>
#> Package version:
#> abind_1.4-5 askpass_1.1 assertthat_0.2.1
#> backports_1.2.1 base64enc_0.1.3 BayesFactor_0.9.12-4.2
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